Prediction Profiles for Nutritional Supplement Use Among Young German Elite Athletes
Bibliographic record
Abstract
Nutritional supplements (NS) are defined as concentrated sources of nutrients and other substances that have a nutritional or physiological effect and that are used in high frequency among athletes. The study aimed to create a prediction profile for young elite athletes to identify those athletes who have a higher relative risk for using NS. The second objective was to examine the hypothesis that the consumption of NS paves a gateway for the use of illicit drugs and doping substances. A self-designed anonymous paper-and-pencil questionnaire was used to examine the prevalence of NS consumption, doping, and illicit drug use in elite athletes with a mean age of 17 years (SD = 4 years). Logistic regression analysis was employed to assess whether NS consumption can be predicted by independent variables (e.g., biographical data, training characteristics, drug consumption behavior) to create the prediction profile for NS use. 55% and 5% of the athletes (n = 536) responded positively to having used NS and illicit drugs, respectively. Nutritional supplement consumption was positively correlated with age (OR: 1.92; CI: 1.21 to 3.05), the desire to enhance performance to become an Olympic or World Champion (OR: 3.72; CI: 2.33 to 6.01), and being educated about NS (OR: 2.76; CI: 1.73 to 4.45). It was negatively correlated with training frequency (OR: 0.55; CI: 0.35 to 0.86) and the use of nicotine (OR: 0.29; CI: 0.1 to 0.74) but did not correlate with illicit drug use and alcohol consumption. The present results show that NS are used on a large scale in elite sports. The prediction profile presented in this article may help to identify those athletes who have a high risk for using NS to plan potential education and prevention models more individually.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".